This study used a novel computational pipeline to exploit draft bacterial genome sequences in order to predict, automatically and rapidly, PCR primer sets for Dickeya spp. that were unbiased in terms of diagnostic gene choice. This pipeline was applied to 16 draft and four complete Dickeya genome sequences to generate >700 primer sets predicted to discriminate between Dickeya at the species level. Predicted diagnostic primer sets for both D. dianthicola (DIA-A and DIA-B) and ‘D. solani’ (SOL-C and SOL-D) were validated against a panel of 70 Dickeya reference strains, representative of the known diversity of this genus, to confirm primer specificity. The classification of the four previously sequenced strains was re-examined and evidence of possible misclassification of three of these strains is presented.